A Hierarchical Spatiotemporal Optimization Model of Customized Bus Routes Considering Time Windows
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摘要: 研究定制公交线网布局及调度优化对增强公交系统吸引力, 提高乘客出行效率具有重要意义。针对定制公交乘客需求点在时间和空间上分布离散的特点, 构建了考虑时间窗的定制公交时空分层优化模型, 并设计遗传算法对模型进行求解。通过渔网与核密度分析对需求点在时间和空间上进行了热点识别, 并实现热点区域聚类分析以及合乘站点分类。基于合乘站点集合, 综合考虑公交容量、线路长度、乘客出行距离构建了线路空间优化模型, 以乘客的时间花费最小作为优化目标构建了线路时间优化模型。以济南市城区定制公交为例对模型的性能进行评估, 案例结果表明: 模型优化后的线路方案, 乘客平均服务覆盖率可达96%, 服务区域内每个时段的单个乘客的平均节省时间为15 min, 公交的平均满载率为90%。Abstract: Studying the layout and scheduling optimization of a customized bus line network has important implications, enhancing the attractiveness of the public transport system and passenger travel. However, the distribution of customized bus passengers' demand points in time and space is discrete, which hinders the bus line design. A timespace hierarchical optimization model of customized buses considering time windows is constructed to solve this problem, and a genetic algorithm is designed to solve the model. The hot spots of demand points are identified in time and space by analyzing the fishing net and kernel density, with the cluster analysis of hot spots and the classification of bus pooling realized. Based on the set of bus pooling, a space optimization model of bus lines is constructed by the bus capacity, line length, and passenger travel distance. A time optimization model of bus lines is constructed by the minimum time cost of passengers. Jinan customized bused are used to evaluate the performance of the model. The results show that the routing scheme is optimized by the model, with an average service coverage rate of passengers of 96%, the average travel time saved by the single passenger in each period of the service area of 15 minutes, and an average load factor of public transport of 90%.
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表 1 定制公交需求数据
Table 1. Demand data of customized buses
序号 云账户 上车点 下车点 上车时间 下车时间 返程时间 上车高德经度/(°) 上车高德纬度/(°) 下车高德经度/(°) 下车高德纬度/(°) 5575 account-180801 龙湖.春江郦城2期 达内济南万达中心 08:20 08:55 18:00 118.051 462 24.584 578 117.131 095 36.686 382 2905 account-274990 响泉路 山东和美华农牧科技股份有限公司 07:00 07:50 17:00 113.075 114 27.779 672 117.323 408 36.707 568 753 account-55545 加州小区24栋 龙奥公交枢纽(公交站) 07:15 08:15 115.568 31 28.842 056 117.137 306 36.654 095 5453 account-235766 文旅城(公交站) 济南水务集团有限公司(顺河东街) 07:30 08:15 106.349 068 29.634 964 117.008 424 36.665 363 3983 account-37676 翡翠华庭二期停车场(入口) 第四人民医院 07:00 07:30 17:15 106.511 279 29.643 769 116.978 9 36.694 529 845 account-32791 春江郦城 火车站(公交站) 07:44 17:55 121.582 684 29.984 336 116.992 531 36.667 751 3956 account-405 杭州市滨江区南环路1656号 杭州市江干区12号大街12~大街16号 08:30 18:30 120.200 954 30.177 261 120.388 441 30.289 529 表 2 需求分类统计
Table 2. Demand classification statistics
ID 上车站点 下车站点 上车时间 下车时间 下车时间 上车区域编号 下车区域编号 account-101566 1 14 06:15 07:30 07:35 2 1 account-121734 1 2 06:30 08:00 08:05 2 3 account-131791 1 4 06:30 07:40 07:45 2 3 account-137716 1 2 07:40 08:40 08:45 2 3 account-160457 1 8 07:30 08:30 08:35 2 1 account-169021 1 5 07:00 07:50 07:55 2 1 account-20811 1 2 07:00 08:00 08:05 2 3 account-20920 1 4 07:00 07:44 07:49 2 3 表 3 区域间不同时段需求人数
Table 3. Number of people needed in different periods between regions
时段 需求人数 1 12 2 44 3 46 4 14 5 0 表 4 普通算法求解的定制公交线路
Table 4. Customized bus routes solved by the common algorithm
编号 线路站点 线路里程/km 载客人数 1 57-82-1-114-117-115 40.28 12 2 57-1-82-114-117-119-122-115 63.08 39 3 95-5-114-115 27.75 5 4 5-1-95-97-114-117-115-122 44.36 46 5 57-1-5-114-115 35.84 14 表 5 本文算法求解的定制公交线路
Table 5. Customized bus route solved by the algorithm proposed in the work
时段 线路站点 线路里程/km 载客人数 06:00—06:30 82-57-1-114-117-115 39.36 12 06:30—07:00 1-114-117-119-122-115 30.68 37 07:00—07:30 5-97-1-95-114-117-115-122 44.94 46 07:30—08:00 57-1-5-114-115 35.16 14 表 6 定制公交线路方案
Table 6. Customized bus-route schemes
时段 线路站点线路里程/km 车型 载客人数 发车时间 06:00—06:30 82-57-1-114-117-115 39.36 小型 12 06:14 06:30—07:00 1-114-117-119-122-115 30.68 中型 37 06:57 07:00—07:30 95-1-5-97-114-117-115-122 35.24 大型 46 07:06 07:30—08:00 57-1-5-114-115 35.16 小型 14 07:30 表 7 线路方案评价表
Table 7. Evaluation of route schemes
时段 服务率/% 人均节省时间/min 满载率/% 06:00—06:30 100 34 80 06:30—07:00 84 7 93 07:00—07:30 100 5 92 07:30—08:00 100 16 93 -
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